TY - GEN
T1 - A Low-Cost Service Node Selection Method in Crowdsensing Based on Region-Characteristics
AU - Peng, Zhenlong
AU - An, Jian
AU - Gui, Xiaolin
AU - Liao, Dong
AU - Gui, Ruo Wei
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Crowdsensing is a human-centred perception model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of accomplishing sensing missions, a proper and cost-effective set of service nodes is needed to perform tasks. In this paper, we propose a low-cost service node selection method based on region features, which builds on relationships between task requirements and geographical locations. The method uses DBSCAN to cluster service nodes and calculate the centre point of each cluster. The region then is divided into regions according to rules of Voronoi diagram. Local feature vectors are constructed according to the historical records in each divided region. When a particular perception task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks.
AB - Crowdsensing is a human-centred perception model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of accomplishing sensing missions, a proper and cost-effective set of service nodes is needed to perform tasks. In this paper, we propose a low-cost service node selection method based on region features, which builds on relationships between task requirements and geographical locations. The method uses DBSCAN to cluster service nodes and calculate the centre point of each cluster. The region then is divided into regions according to rules of Voronoi diagram. Local feature vectors are constructed according to the historical records in each divided region. When a particular perception task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks.
KW - Crowdsensing
KW - Local feature vector
KW - Service node selection
UR - https://www.scopus.com/pages/publications/85064046125
U2 - 10.1007/978-3-030-15093-8_25
DO - 10.1007/978-3-030-15093-8_25
M3 - 会议稿件
AN - SCOPUS:85064046125
SN - 9783030150921
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 345
EP - 356
BT - Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers
A2 - Li, Shijian
PB - Springer Verlag
T2 - 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018
Y2 - 11 May 2018 through 13 May 2018
ER -